ML project structure in ML Python - Model Pipeline Trace

Model Pipeline - ML project structure

This ML project structure organizes the steps from raw data to model predictions. It helps keep the work clear and easy to follow.

Data Flow - 7 Stages

1Raw Data Collection

N/A→Gather data from sources like files, databases, or sensors→1000 rows x 10 columns

CSV file with 1000 rows and 10 columns of customer info

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2Data Cleaning

1000 rows x 10 columns→Remove missing values and fix errors→980 rows x 10 columns

Dropped 20 rows with missing age values

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3Feature Engineering

980 rows x 10 columns→Create new features and select important ones→980 rows x 12 columns

Added 'age_group' and 'income_per_person' columns

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4Train/Test Split

980 rows x 12 columns→Split data into training and testing sets→Training: 784 rows x 12 columns, Testing: 196 rows x 12 columns

80% training and 20% testing split

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5Model Training

784 rows x 12 columns→Train model on training data→Trained model object

Random Forest model trained on training set

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6Model Evaluation

Trained model, 196 rows x 12 columns test data→Evaluate model performance on test data→Accuracy, precision, recall scores

Model accuracy = 85%

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7Prediction

New data 10 rows x 12 columns→Use trained model to predict outcomes→Predictions array of length 10

Predicted customer churn: [0,1,0,0,1,1,0,0,1,0]

Training Trace - Epoch by Epoch

Epoch 1: 0.65 #######
Epoch 2: 0.50 #####
Epoch 3: 0.40 ####
Epoch 4: 0.35 ###
Epoch 5: 0.30 ##

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning with moderate accuracy
2	0.50	0.72	Loss decreases and accuracy improves
3	0.40	0.80	Model continues to improve
4	0.35	0.83	Training converging with better accuracy
5	0.30	0.86	Final epoch with best performance

Prediction Trace - 4 Layers

Layer 1: Input new data sample

Layer 2: Feature scaling

Layer 3: Model prediction

Layer 4: Thresholding

Model Quiz - 3 Questions

What happens to data during the 'Data Cleaning' stage?

A Train the model

B Split data into train and test

C Remove missing or incorrect data

D Make predictions on new data

Key Insight

A clear ML project structure helps organize data and model steps. It ensures data flows logically from raw input to predictions, and training improves model performance steadily.

Practice

1. Which folder in an ML project should you use to store the original, untouched data files?

easy

A. Raw data folder

B. Processed data folder

C. Models folder

D. Scripts folder

Solution

1. Step 1: Understand the purpose of raw data folder

   The raw data folder is meant to keep the original data safe and unchanged.

2. Step 2: Differentiate from processed data folder

   The processed data folder contains cleaned or transformed data, not the original files.

3. Final Answer:

   Raw data folder -> Option A

4. Quick Check:

   Original data = Raw data folder [OK]

Hint: Original files go to raw data folder to keep them safe [OK]

Common Mistakes:

• Putting original data in processed folder
• Mixing models with data files
• Saving scripts in data folders

2. Which of the following is the correct way to organize code in an ML project?

easy

A. Put all code, notebooks, and models in one folder

B. Keep processed data and scripts mixed in one folder

C. Separate notebooks for exploration and scripts for training

D. Save models inside the raw data folder

Solution

1. Step 1: Identify the role of notebooks and scripts

   Notebooks are for exploring data and ideas; scripts are for running training and processing tasks.

2. Step 2: Understand folder separation

   Keeping notebooks and scripts separate helps keep the project organized and clear.

3. Final Answer:

   Separate notebooks for exploration and scripts for training -> Option C

4. Quick Check:

   Notebooks ≠ scripts folder [OK]

Hint: Use notebooks for exploring, scripts for running training [OK]

Common Mistakes:

• Mixing notebooks and scripts in one folder
• Saving models in data folders
• Putting all files in one folder

3. Given this ML project folder structure:

project/
├── data/
│   ├── raw/
│   └── processed/
├── notebooks/
├── scripts/
└── models/

Where should you save a trained model file after training?

medium

A. data/raw/

B. scripts/

C. notebooks/

D. models/

Solution

1. Step 1: Understand folder purposes

   The models folder is designed to store trained model files for reuse and deployment.

2. Step 2: Exclude other folders

   Data folders are for data files, notebooks for exploration, scripts for code execution, so models belong in models/.

3. Final Answer:

   models/ -> Option D

4. Quick Check:

   Trained model files go to models folder [OK]

Hint: Save trained models in the models folder [OK]

Common Mistakes:

• Saving models in data folders
• Putting models inside notebooks folder
• Confusing scripts folder with models

4. You find that your ML project has raw data and processed data mixed in the same folder. What is the best fix?

medium

A. Rename all files to indicate if they are raw or processed

B. Move raw data to a separate 'raw' folder and processed data to 'processed' folder

C. Delete the processed data and keep only raw data

D. Keep all data in one folder but add a README explaining the files

Solution

1. Step 1: Identify the problem

   Mixing raw and processed data risks overwriting or confusion about data versions.

2. Step 2: Organize data properly

   Separating raw and processed data into different folders keeps original data safe and processed data clear.

3. Final Answer:

   Move raw data to a separate 'raw' folder and processed data to 'processed' folder -> Option B

4. Quick Check:

   Separate raw and processed data folders [OK]

Hint: Keep raw and processed data in separate folders [OK]

Common Mistakes:

• Deleting processed data unnecessarily
• Only renaming files without moving
• Relying on README without folder separation

5. You want to share your ML project with a teammate. Which folder should you exclude to keep the project size small and avoid sharing large temporary files?

hard

A. outputs/

B. data/raw/

C. models/

D. notebooks/

Solution

1. Step 1: Identify large temporary files

   The outputs folder usually contains temporary results like logs, plots, or intermediate files that can be large and rebuilt.

2. Step 2: Decide what to share

   Models, raw data, and notebooks are important for reproducing work, so outputs can be excluded to save space.

3. Final Answer:

   outputs/ -> Option A

4. Quick Check:

   Exclude outputs folder to reduce project size [OK]

Hint: Exclude outputs folder to avoid sharing large temp files [OK]

Common Mistakes:

• Excluding raw data which is needed
• Removing models folder by mistake
• Not excluding any folder and sharing everything